
{"references": ["Tsuchida, Shuhei, Satoru Fukayama, and MasatakaGoto. \"Query-by-dancing: a dance music retrieval system based on body-motion similarity.\" International Conference on Multimedia Modeling. Springer, Cham, 2019.", "Cao, Zhe, et al. \"OpenPose: realtimemulti-person 2D pose estimation using Part Affinity Fields.\" IEEE transactions on pattern analysis and machine intelligence 43.1 (2019): 172-186.", "Lin Peng. 2020. Why is re-enactment critical in the research of Confucian Rites", "Kenderdine, Sarah, and Jeffrey Shaw. \"Archives in Motion.\" Museum and Archive on the Move: Changing Cultural Institutions in the Digital Era, edited by Oliver Grau Wendy Coonesand Viola R\u00fchse(2017): 211-233."]}
Please refer to the up-to-date version of this poster at https://doi.org/10.5281/zenodo.4706898
embodied knowledge, machine learning, computational archive, FAIR
embodied knowledge, machine learning, computational archive, FAIR
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
